Python is an excellent language for rapid prototyping of algorithms and programs in order to determine the feasibility of a task, but oftentimes fails to meet the run time and memory usage requirements needed for deployment to production. This presentation will discuss tools within the Python ecosystem for profiling Python code to identify memory and run time hot spots. Techniques will be presented which can be used to improve the performance of Python code by utilizing the information provided by these tools. Real world examples will be provided from the development and optimization of radar algorithms for use in the Python ARM Radar Toolkit (Py-ART), an open source library for working with weather radar data in Python.
Jonathan Helmus is a scientist and advanced algorithms engineer at Argonne National Laboratory where he develops software for the Atmospheric Radiation Measurement (ARM) climate research facility. He is the lead developer of Py-ART, an open source toolkit for analysis of weather radar data in Python as well as having contributed to a number of other Scientific Python modules. Jonathan completed a postdoc at the University of Connecticut Health Center after receiving his Ph.D. in Chemical Physics from The Ohio State University.
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